1. Minimalist Architectures for Large-Scale Sensor Networks Upamanyu Madhow ECE Department University of California, Santa Barbara Funding Sources

2. Research Overview Sensor Networks – Scalability: size and energy – Camera Networks – Fundamentals of Tracking Nextgen Wireless – Millimeter wave communication Indoor WPAN: Gigabit speeds Outdoor LOS: Multigigabit speeds – Scalability and QoS in multihop wireless networks – Cognitive radio architectures and signal processing Multimedia security – Data hiding – Steganalysis Today’s focus

3. Collaboration is key to progress Ad hoc networks (Prof. Elizabeth Belding-Royer--CS) Electronics & Photonics (Prof. Mark Rodwell--ECE) Computer Vision (Prof. B. S. Manjunath--ECE) Controls (Prof. Joao Hespanha--ECE) Computational Geometry (Prof. Subhash Suri--CS) Imaging Sensor Nets Distributed Beamforming Multimedia Security Wireless QoS Tracking with Binary Sensors Distributed Compression Camera networks Ultra high-speed wireless comm Source Coding (Prof. Ken Rose--ECE) Cognitive radio Signal Processing (Prof. Kannan Ramchandran--Berkeley)

4. Who is doing the work? Bharath Ananthasubramaniam: Signal Processing for Imaging Sensor Nets Ibrahim El-Khalil: Data hiding and steganalysis Raghu Mudumbai: Distributed beamforming, camera nets, tracking, cross-layer design Mike Quinn: Camera networks Anindya Sarkar: Data hiding and steganalysis Munkyo Seo: IC design for Imaging Sensor Nets Jaspreet Singh: Distributed compression, high-speed comm Sumit Singh: Wireless QoS, protocols for mm wave radio Ben Wild (UC, Berkeley): Distributed beamforming prototype

5. Sensor Nets: the CENS view Micro-sensors, on- board processing, wireless interfaces feasible at very small scale--can monitor phenomena “up close” Enables spatially and temporally dense environmental monitoring Embedded Networked Sensing will reveal previously unobservable phenomena Contaminant TransportEcosystems, Biocomplexity Marine Microorganisms Seismic Structure Response Slide courtesy of Dr. Deborah Estrin (CENS-UCLA): http://cens.ucla.edu

6. Sensor Nets Today Berkeley motes continue to prove their worth – Impact on science – Promising for DoD and security applications Scalability of flat architectures limited to 100s of nodes – Enough for many applications – Hierarchical architectures can help But we are far from the sci-fi vision of Smart Dust – Hundreds of thousands of randomly deployed sensors – Dumb sensors that get smarter by working together

7. Scale requires minimalistic design Scaling in Space – Sensors have small coverage area (e.g., bio/chem) – Large areas must be covered – Large deployments must be automated – Need minimalistic network protocols Energy Scaling – Need long battery life or batteryless operation – Minimalistic mechanisms for cooperative communication can significantly enhance performance Scaling in functionality – Small, inexpensive, noisy, failure-prone microsensors – Need minimalistic sensing models

8. Today’s talk Spatial scaling: Imaging sensor nets – Have been talking about concept since 2004 – Today: ongoing prototyping effort Energy scaling: Distributed transmit beamforming – Gains from non-ideal beamforming explored in 2004 – Today: a method that works and prototyping results Cost/functionality scaling: Tracking with binary sensors – What can we do if a sensor can only say yes or no? – Today: Fundamental limits, minimal descriptions, algorithms Students Involved Bharath Ananthasubramaniam, Munkyo Seo Raghu Mudumbai, Ben Wild Raghu Mudumbai, Nisheeth Srivastava

9. Imaging Sensor Nets

10. Why very large scale sensor nets? Coverage of large outdoor spaces Limited sensing range per sensor Applications span DoD, homeland security, basic science, commercial sectors – Battlefield situational awareness with large-scale random deployment – Border policing – Detection of chemical/biological agents on city scale – Planetary exploration – Monitoring soil moisture, pesticide/fertilizer levels How to scale? How to localize? Multihop inter-sensor networking does not apply

11. Inspiration from Imaging Advantages of Imaging Systems Effective # of sensors = number of pixels in image: set by system optics Millions to billions of addressable pixels Don't need to build millions of sensors, don't need to recharge sensor batteries Limitations of Imaging Systems Application limited to phenomena with strong enough electromagnetic signature Synthetic Aperture, Focal plane array, etc. passive or active imaging of surface microwave, mm-wave, infrared, visible absorbtion/emission spectroscopy → chemical species optical surface energy flux temperature IR LANDSAT images

12. Imaging Sensor Nets: the Concept Field of simple, low-power sensors dispersed across field of view Cast on ground from truck, plane, or satellite Sensor as pixels (“dumb dust”) Electronically reflect, with data modulation, beacon from collector (“virtual radar”) Minimal functionality: no GPS, no inter-sensor networking Lifetime of year on watch cell battery Sophisticated collector Radar and image processing, multiuser data demodulation Joint localization and data collection Range varies from 100m to 100 km Active versus “passive” sensors, collector characteristics vast numbers of low-complexity "dumb" pixels sensor + RF transducer + antenna. collector: satellite base station on UAV Sensor field

13. Prototype with stationary collector Millimeter wave carrier frequencies Narrow beam with moderate size collector antenna Small sensor form factor Key challenges Low-power, low-cost sensor ICs: mm-wave in CMOS Collector signal processing

14. Collector Beacon with location code Sensor field with active sensors and inactive sensors Active sensor reflects beacon Inducing a radar geometry

15. Basic Link Diagram PRBS DATA Jointly detect DATA and DELAY Collector Sensor BPSK modulation DOWN-LINK UP-LINK Freq. shift to filter out ground return

16. Hierarchy of Challenges Circuit-level Technology-level Link-level Detection/Estimation Imaging Algorithm Application interface Mm-wave Design IC technology Substrate/Package/Integration Signal/Image Processing Low-power, mm-wave circuit design Efficient, low-loss antenna Interconnect Sensor architecture Link budget calculation Collector phased array design Estimation for asynchronous modulation Limited precision samples Nonideal beam patterns Integrating soft info

17. Sensor IC functionality Semi-Passive ImplementationAmplify-Reflect Implementation IC implementation in MOSIS-accessible CMOS and SiGe processes Very small antennas (~5 mm dimension) Inexpensive packaging

18. Zeroth order Link Budget Down-linkUp-link 75 GHz carrier Collector with 1 meter diameter antenna, 100 mW transmit antenna 100 Kbps using QPSK/BPSK at BER of 10 -9 300 m range for semi-passive sensor 100 km range for active sensor with 5 mW transmit power Downlink Uplink (bottleneck)

19. Collector Baseband Processing Correlate signal with location code to estimate delay, accounting for Residual frequency modulation Low rate data Multiple sensors in each scan Demodulation algorithm for asynchronous data modulation Software implementation Limited sample precision

20. Collector Imaging Algorithm : location code : beam pattern

21. Normalized resolution Root Mean Squared Error in X & Y coordinates versus SNR SAR-like processing gives resolution far better than chip duration Multiple collectors can be used to equalize x & y resolution

22. Localization for large sensor density Single Sensor Algorithm + SIC – works well 25 sensors 100 sensors

23. Brassboard concept validation

24. Collector block diagram Note: Sensor frequency shifts reflection by 50 MHz

25. Brassboarded collector and sensor Collector Senso r

26. Collector Brassboard RX-Antenna (~2 degree beamwidth) TX Antenna (~20 degree beamwidth) Waveguide-based Up-down converter 2-dimensional Antenna scanner

27. Semi-Passive Sensor Brassboard Baseband electronics (1~100Kbps local data plus 50MHz shifting-LO) FRONT (ANT + modulator) BACK (ANT + modulator) 60GHz carrier Modulated 60 GHz carrier 50MHz +data

28. Indoor Radio Experiment Up to ~10m of range achievable. 2 nd -Gen setup capable up to >100m. – Using active sensors (w/ gain) and higher-gain collector antenna. Passive sensor on a cart Collector system

29. Preliminary results Data for 3 ranges – 4 ft.,6 ft. and 8 ft. Data transmitted – 16-bits 1110101001101100 Range Resolution (chip length) = 7.5 m. 1kbps10kbps Range est. (4 ft = 1.2 m)11.4375 m11.4725 m Range est. (6 ft = 1.8 m) [Ref.]12.0000 m Difference (2 ft = 0.6 m)0.5625 m0.5275 m Range est. (8 ft = 2.4 m)12.5625 m Difference (2 ft = 0.6 m)0.5625 m Freq. offset~11.8 kHz Sub-chip precision achievable using averaging Collector circuit delays are predictable and easy to calibrate out BER ~ 10 -2

30. Future Work Collector Hardware/Processing – Azimuth data collection – from computer by controlling antenna pointing direction – Azimuth processing to improve effective SNR/ Range – Upgrade PA to 200mW (full power) to perform outdoor ranging experiments. (with FCC permission) – Integrate collector components into IC Sensor ICs Complete Design of “Passive” Sensor CMOS IC Work towards the Active ICs

31. Imaging Sensor Nets: Current Status Lab-scale mm wave experiment to verify link budgets – Basic concept has been verified – Baseband software (version 1) has been developed Brassboard to-dos – Azimuth data collection – Higher power transmission to increase range (awaiting FCC OK) IC design for semi-passive sensor IC design for active sensor – Need creative solution for isolation problems Many open system level issues – Data representation/compression, redundancy – Exploiting multiple collectors – Sensor-driven imaging sensor nets

32. Distributed Beamforming

33. Energy Efficiency via Distributed Beamforming Distributed beamforming can increase range or cut power – Rec’d power = (A + A + …+A) 2 = N 2 A 2 if phases line up – Rec’d power = N A 2 if phases don’t line up (+ fading) Can use low frequencies for better propagation – Large “antenna” using natural spatial distribution of nodes Diversity BUT: RF-level sync is hard! Today: Sync using RX feedback (analysis & prototype) Receiver 1  j e 2  j e SNR feedback

34. Feedback Control Mechanism Initially the carrier phases are unknown Each timeslot, the transmitters try a random phase correction Keep the corrections that increase SNR, discard the others Carrier phases become more and more aligned Phase coherence achieved in time linear in number of nodes Typical phase evolution (10 nodes)

35. Experimental Prototype Receiver Transmitters 1KHz Feedback channel

36. Transmitter block diagram 12 Bit DAC FPGA 200Hz, 1 bit feedback from Receiver 12 Bit DAC Cos(2  (904e6)t) R*cos(2  (904e6)t+  ) 64 point Sine Table 64 point Cosine Table 1024 point Random number {±1} Table Phase Counter Best Phase A B sin(2  (904e6)t) R =  (A 2 +B 2 ),  = arctan(B/A)

37. Receiver block diagram 904 MHz Bandpass Filter, 20MHz BW cos(2  (904e6+f IF )t ) 904 MHz Signal sin(2  (904e6+f IF )t ) 1MSPS, 16 Bit ADC 1MSPS, 16 Bit ADC 20kHz IF to avoid problems with Direct Conversion to DC 12 Bit DAC Oscilloscope FPGA ( · ) 2 5000 Sample average 200Hz 1 bit feedback to beamformers Power (i) Power (i) >? Power(i-1,..,i- M-1) {M=4 for results shown} time Power

38. Received Power Time Need to get data for this.

39. Conclusions from prototype Distributed beamforming works! Key technical challenges – Phase jitter: Highly sensitive to PLL loop-filter – Power measurement for modulated signals Limitations – Small-scale experiment (only 3 transmitters) – Static channel conditions

40. Towards an analytical model Empirical observation: convergence is highly predictable y[n+1] α. y[n] x1x1 x2x2 Net effect of phase perturbations What can we say about the distributions of x 1 and x 2 ? (without knowing all the individual transmitter phases)

41. Key idea: statistical approach Received signal proportional to –Infinitely many possible  i [n] for any given y[n] Analogy with statistical physics –Given total energy i.e. temperature What is the energy of each atom? More interesting: how many atoms have a energy, E –Concept of Macrostates –Distribution of energy is fixed –Maxwell-Boltzmann distribution –Density ~ exp(-E/kT)

42. The “exp-cosine” distribution Initially  i [0] is uniform in (- π, π ] The phases  i [n] get more and more clustered Given, what is the distribution of  i [n]? –“Typical” distribution closest in KL distance to uniform –The Conditional Limit Theorem The “exp-cosine” distribution

43. Exp-cosine matches simulations N = 500 transmitters

44. Implications of analytical framework Accurate analytical predictions for moderately large N – Evolution of phase distribution – Convergence rate Optimal choice of distribution of phase perturbations – To maximize convergence rate Proof of scalability – Convergence time linear in N

45. Accurate prediction of trajectory

46. Effect of optimization 200 transmitters Fixed uniform distribution vs. uniform distribution optimized at each slot Optimize pdf for δ i at each iteration restrict to uniform pdf: δ i ~uniform[- δ 0,+ δ 0 ] Choose δ 0 to maximize E( Δ y[n]), given y[n]

47. Scalability and Convergence Phase perturbation not optimized Uniform over (-2 o,2 o ) Phase perturbation optimized Scalable: Convergence is linear in the number of nodes N (provably so for optimized phase perturbations)

48. Tracking time-variations Must adapt fast enough to track channel Too fast an adaptation causes loss in phase coherence Should we maximize the mean SNR? Should we minimize fluctuations? How can we trade them off?

49. Analytical framework for time variations Statistical distribution still applies – “exp-cosine” phase distribution for large N – Received SNR is a Markov random process – Analytically derive steady-state distribution Excellent match with simulations!

50. Distributed Beamforming: Current Status We now know it works – 3 node prototype gives 90% of maximum possible gains Analytical framework accurately predicts performance – Need simple rules of thumb for time-varying channels – Better justification of exp-cosine derivation Applications go beyond sensor nets – Wireless link protocols building on distributed beamforming? Generalization to other distributed control tasks?

51. Tracking with binary proximity sensors

52. Tracking with binary sensors Minimalistic model appropriate for microsensors – Sensor says target present or absent – Appropriate for large-scale deployments How well can we track with a network of binary sensors? – Fundamental limits – Minimal path descriptions – Efficient geometric algorithms

53. The Geometry of Binary Sensing Target Path Sensor Outputs Localization patches Localization arcs

54. Results Attainable resolution ~ 1/(sensor density*sensing range) Spatial low pass filtering – Path variations faster than resolution cannot be tracked – We can track “lowpass” version of the path OccamTrack algorithm – Constructs minimal piecewise linear path representations – Provides velocity estimates for “lowpass” version Robustness to sensing range variation – Original OccamTrack can get into trouble – Particle filtering algorithm + Geometric clean-up Lab-scale experiments

55. How a trajectory is localized Max patch size at least 1/(density*range) for any deployment Patch size of the order of 1/(density*range) for Poisson deployment Proof of resolution theorems

56. Minimal representation: OccamTrack Greedy algorithm for piecewise linear representation: Draw lines stabbing localization arcs that are as long as possible

57. Spatial Lowpass Filtering Cannot capture rapid variations Can only reconstruct “lowpass” version of path Justifies piecewise linear representation

58. Velocity estimation and minimal representation Which path should we use to estimate (lowpass version of) velocity? We can be off by a factor of two! A simple formula: dv/v = dL/L Proposition: If piecewise linear approx works well, then velocity estimate is accurate.

59. OccamTrack Velocity Estimate OccamTrack Output Weighted Centroid Output (Kim et al, IPSN 2005) Simulation Results

60. Fundamental Resolution Limits Theoretical resolution attained by both regular and random deployment

61. Handling non-ideal sensing Detected Not detected ? A simple model Localization patch = intersection of outer circles with complements of inner circles Particle filtering algorithm provides robust performance Geometric clean-up provides minimal description Acoustic sensors are unreliable & unpredictable Low pass filter observations Experiment with acoustic sensor

62. Lab scale mote experiment Acoustic sensors, tone generator

63. OccamTrack Particle Filter Particle Filter + Geometric OccamTrack with ideal sensing Results from Lab-Scale Mote Experiment Non-ideal sensor response

64. Tracking with Binary Sensors: Current Status Low-cost microsensors can be very useful for tracking – Small range can be compensated for by dense deployment – Random deployment works – Fundamental limits identified and attained – Algorithms can be made robust to non-ideal sensing Current work focuses on multiple targets – Can we count them reliably? – How well can we track?

65. Challenges ahead: a sampling Sensor Networks – Imaging Sensor Nets: making them happen – Architectures for in-network processing and collaboration – Comprehensive solutions for flagship applications – The role of distributed control – Visual sensor networks Ultra high-speed communication – Cross-layer design of mm wave networks – The ADC bottleneck Multihop multimedia comm – Video through the home – Voice in emergency plug-and-play networks